Hyperuricaemia (HUA) is a metabolic disorder resulting from the dysregulation of purine metabolism. It is closely associated with gout and various metabolic syndromes, representing an increasing global public health challenge. Current treatment approaches for HUA and gout generally involve the lifelong administration of urate-lowering agents to maintain optimal serum urate concentrations. However, poor patient adherence, often due to potential hepatorenal toxicity, frequently leads to disease relapse. Recent evidence indicates that the gut microbiota plays a significant role in maintaining urate homeostasis through multiple mechanisms, including the modulation of purine metabolism, urate catabolism and excretion, regulation of inflammatory responses, and preservation of intestinal barrier integrity. These findings highlight the gut microbiota as a promising novel therapeutic target. This review synthesizes recent progress in three key areas: (1) the relationship between the gut microbiota and HUA; (2) microbial mechanisms underlying urate-lowering effects, such as microbial purine and urate metabolism, regulation of urate transporters like ABCG2, and production of anti-inflammatory metabolites; and (3) microbiota-based therapeutic interventions, including probiotics, engineered bacterial strains, fecal microbiota transplantation, and pharmabiotic strategies. Additionally, we explore the translational potential of microbiota modulation in clinical settings and outline directions for future research. By integrating mechanistic understanding with therapeutic innovation, this review offers researchers and clinicians a comprehensive framework for advancing microbiota-targeted approaches in the management of hyperuricaemia.

Heart failure with preserved ejection fraction (HFpEF) is a complex and heterogeneous clinical condition characterized by the interplay of malnutrition and immune inflammation, especially in elderly patients. The CRP-Albumin-Lymphocyte (CALLY) index, a novel composite indicator reflecting immune inflammation and nutritional status, has not yet been validated as a prognostic tool in elderly patients with HFpEF. This retrospective study included 320 elderly patients hospitalized at the Air Force Medical Center from October 2016 to April 2019 due to HFpEF. Patients were stratified into the all-cause mortality and the survival groups according to follow-up outcomes. Kaplan-Meier analysis and Cox regression were performed to identify risk factors associated with poor prognosis. Additionally, we constructed and evaluated a nomogram based on the CALLY index to predict survival rates. During the follow-up period, 137 cases (42.81 %) of patients experienced all-cause mortality. Kaplan-Meier survival curves and Cox regression analysis revealed that a lower CALLY index (HR 0.811, 95 % CI 0.714-0.921, P = 0.001) was independently associated with adverse prognosis in elderly patients with HFpEF. The nomogram incorporating the CALLY index exhibited robust predictive performance for predicting 1-year, 3-year, and 5-year survival outcomes. Our findings demonstrate that the CALLY index is an independent predictor of long-term mortality in elderly patients with HFpEF. The developed nomogram incorporating the CALLY index could effectively predict survival probabilities.

Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Necroptosis contribute to the pathogenesis of myocardial ischemia/reperfusion (MI/R) injury. Ginsenoside Rg2 has been reported to have cardioprotective effects against MI/R injury; however, the underlying mechanism remains unclear. This work aimed to investigate the effect of ginsenoside Rg2 on necroptosis induced by MI/R and to explore the mechanism. In this study, hypoxia/reoxygenation (H/R) injury model was established in H9c2 cells.In vivo, male C57/BL6 mice were subjected to myocardial ischemia 30 min/reperfusion 4 h. Rg2 (50 mg/kg) or vehicle was intravenously infused 5 min before reperfusion. Cardiac function and the signaling pathway involved in necroptosis were investigated. Compared with H/R group, Rg2 significantly inhibited H/R-induced cardiomyocyte death. Rg2 treatment effectively inhibited the phosphorylation of RIP1, RIP3, and MLKL in H/R cardiomyocytes, and inhibited RIP1/RIP3 complex (necrosome) formation. In mice, Rg2 treatment manifested significantly lower ischemia/reperfusion (I/R)-induced myocardial necroptosis, as evidenced by decrease in phosphorylation of RIP1, RIP3, and MLKL, inhibited lactate dehydrogenase (LDH) release and Evans blue dye (EBD) penetration. Mechanically, an increased level of tumor necrosis factor α (TNFα), interleukin (IL)-1β, IL-6, and MCP-1 were found in MI/R hearts, and Rg2 treatment significantly inhibit the expression of these factors. We found that TNFα-induced phosphorylation of RIP1, RIP3, and MLKL was negatively correlated with transforming growth factor-activated kinase 1 (TAK1) phosphorylation, and inhibition of TAK1 phosphorylation led to necroptosis enhancement. More importantly, Rg2 treatment significantly increased TAK1 phosphorylation, enhanced TAK1 binding to RIP1 while inhibiting RIP1/RIP3 complex, ultimately reducing MI/R-induced necroptosis. These findings highlight a new mechanism of Rg2-induced cardioprotection: reducing the formation of RIP1/RIP3 necrosome by regulating TAK1 phosphorylation to block necroptosis induced by MI/R.

Background: The long-term prognosis of heart failure with preserved ejection fraction (HFpEF) is influenced by malnutrition. Currently, there’s a deficit in objective and comprehensive nutritional assessment methods to evaluate the nutritional status and predicting the long-term outcomes of HFpEF patients. Methods: Our retrospective study included two hundred and eighteen elderly HFpEF patients admitted to the cardiovascular ward at the Air Force Medical Centre from January 2016 to December 2021. Based on follow-up outcomes, patients were categorized into all-cause death (99 cases) and Survival (119 cases) groups. We compared general data, laboratory results, and nutritional indexes between groups. Differences in subgroups based on Triglyceride-Total Cholesterol-Body Weight Index (TCBI), Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and Controlled Nutrition Score (CONUT) were analyzed using Kaplan-Meier survival curves and log-rank test. COX regression was used to identify all-cause mortality risk factors, and the predictive accuracy of the four nutritional indices was assessed using receiver operating characteristic (ROC) curves and Delong test analysis. Results: A total of 101 (45.41%) HFpEF patients experienced all-cause mortality during 59.02 ± 1.79 months of follow-up. The all-cause mortality group exhibited lower GNRI and PNI levels, and higher CONUT levels than the Survival group (p < 0.05). Kaplan-Meier analysis revealed lower cumulative survival in the low GNRI (≤96.50) and low PNI (≤43.75) groups, but higher in the low CONUT (≤2) group, compared to their respective medium and high-value groups. Multifactorial COX regression identified low PNI (≤43.75) as an independent all-cause mortality risk factor in elderly HFpEF patients. ROC and Delong’s test indicated PNI (area under the curve [AUC] = 0.698, 95% confidence interval [CI] 0.629–0.768) as a more effective predictor of all-cause mortality than TCBI (AUC = 0.533, 95% CI 0.456–0.610) and CONUT (AUC = 0.621, 95% CI 0.547–0.695; p < 0.05). However, there was no significant difference compared to GNRI (AUC = 0.663, 95% CI 0.590–0.735; p > 0.05). Conclusions: In elderly HFpEF patients a PNI ≤43.75 was identified as an independent risk factor for all-cause mortality. Moreover, PNI demonstrates superior prognostic performance in predicting all-cause mortality in elderly patients with HFpEF when compared to TCBI, GNRI, and COUNT.

Background: Several objective and comprehensive nutritional assessment methods have been used for predicting adverse outcomes in elderly patients with various diseases. However, their predictive value for long-term adverse outcomes in elderly patients with atrial fibrillation (AF) is unclear. This study aimed to explore the prognostic significance of the four nutritional indices, namely Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Controlling Nutritional Status (CONUT), and High-Sensitivity Modified Glasgow Prognostic Score (HS-mGPS), in evaluating the long-term prognosis in elderly patients with AF. Methods: This retrospective study was conducted at a single center and included a total of 261 elderly patients with AF from December 2015 to December 2021. Patients were divided into all-cause death and survival groups based on the follow-up results. Kaplan–Meier analysis and COX regression were conducted to explore the relationship between all-cause mortality and nutritional scores. In addition, Receiver Operating Characteristic (ROC) curves were used to compare the predictive value of the four nutritional screening tools for the endpoint event. Results: During the follow-up period, 119 cases (45.59%) of all-cause mortality were recorded. The cumulative incidence of all-cause death was significantly higher in participants with a lower PNI, lower GNRI, higher CONUT, and higher HS-mGPS levels. And the low PNI (HR 1.325, 95% CI 1.032– 2.857, P=0.025) and the high HS-mGPS (HR 2.166, 95% CI 1.281– 4.326, P=0.023) groups were independently and significantly associated with increased risk of all-cause death analyzed by multivariate COX regression. Additionally, PNI showed the best area under the curve value (AUC: 0.696, 95% CI 0.632– 0.760 P < 0.001) for the prediction of all-cause mortality compared with the other nutritional indices. Conclusion: Among the four nutritional risk screening tools, PNI might be a simple and useful indicator in predicting the long-term prognosis of elderly patients with AF.